Data Visualization for Chronic Neurological and Mental Health Condition Self-management: Systematic Review of User Perspectives

Background Remote measurement technologies (RMT) such as mobile health devices and apps are increasingly used by those living with chronic neurological and mental health conditions. RMT enables real-world data collection and regular feedback, providing users with insights about their own conditions. Data visualizations are an integral part of RMT, although little is known about visualization design preferences from the perspectives of those living with chronic conditions. Objective The aim of this review was to explore the experiences and preferences of individuals with chronic neurological and mental health conditions on data visualizations derived from RMT to manage health. Methods In this systematic review, we searched peer-reviewed literature and conference proceedings (PubMed, IEEE Xplore, EMBASE, Web of Science, Association for Computing Machinery Computer-Human Interface proceedings, and the Cochrane Library) for original papers published between January 2007 and September 2021 that reported perspectives on data visualization of people living with chronic neurological and mental health conditions. Two reviewers independently screened each abstract and full-text article, with disagreements resolved through discussion. Studies were critically appraised, and extracted data underwent thematic synthesis. Results We identified 35 eligible publications from 31 studies representing 12 conditions. Coded data coalesced into 3 themes: desire for data visualization, impact of visualizations on condition management, and visualization design considerations. Data visualizations were viewed as an integral part of users’ experiences with RMT, impacting satisfaction and engagement. However, user preferences were diverse and often conflicting both between and within conditions. Conclusions When used effectively, data visualizations are valuable, engaging components of RMT. They can provide structure and insight, allowing individuals to manage their own health more effectively. However, visualizations are not “one-size-fits-all,” and it is important to engage with potential users during visualization design to understand when, how, and with whom the visualizations will be used to manage health.


Characteristics and Critical Appraisal of Included
Characteristics and critical appraisal of included studies Critical appraisal was conducted using the mixed-methods appraisal tool (MMAT), and studies were appraised as qualitative (n= 15) or mixed-method (n=14) studies [25,26]. The MMAT does not provide a total quality score. Rather, it prompts the user to assess a study's quality according to a series of questions to which the review can answer "Yes," "No," or "Can't Tell" (Noted as "Y," "N," and "?," respectively, in Tables S2 and S3). We then categorized studies as 'key papers' which are conceptually rich with an important contribution to the synthesis, 'satisfactory papers' which are methodologically acceptable but provide only moderate value or contribution to the synthesis, and 'fatally flawed papers' which contain major methodological flaws [27,28]. In addition, we note 'minimal impact papers' in which relevant data was sparse and which provided minimal contribution to the synthesis.   Coding frame Table S4: Coding frame developed for this study. Codes were identified, organized, and grouped into descriptive themes (ie, "Visualization Design", shown in gray), primary codes (ie, "Format", listed by row), and sub codes (ie, "Numbers/text," "Graphical", shown as bullet points under primary codes).

Themes, codes & sub codes Code definition
Desire for visualization Desire for data visualization • Dissatisfaction with lack of visualization Indication of a desire (or lack thereof) for visualizations of data derived from RMT, even when not originally provided in an RMT Description of the form of the data visualization, either provided by the authors or desired/described by participants. Sub codes were used to describe common types of formats, such as line graphs (Graphical), numeric displays (Numbers/text), etc.

Selecting, manipulating, comparing data streams
Any discussion of selecting (e.g., choosing which data to visualize), manipulating (e.g., zooming in, analyzing or filtering), or comparing data streams through or within visualizations

Context & annotation • Internal • External
Any discussion related to contextual information required to interpret and use the visualization. Context could be internal (ie, self-reported notes to explain a specific score), or external (information provided by the app to help users interpret and act on the visualized data).

Simplicity vs. Complexity
Any discussion regarding service users' preference on how simple or complex a visualization should ideally be. This code included preferences, general comments, and tensions relating to simplicity and complexity.

Image, shape, & color • Convey meaning • Cause feelings
Any discussion regarding ways in which image, shape, and color affected users' experiences or preferences of a visualization. Often, color "conveyed meaning," (ie, categorization of data), or "caused feelings" in a positive or negative manner.

Timeliness of data access and visualization Timeliness
Any description of how often or how quickly individuals preferred to access and visualize their data.

Impact of visualization
Any discussion of real or hypothetical use of visualizations to communicate with others, including the rationale, experiences, preferences, and outcomes of using visualizations in those contexts. Sub codes included common groups with whom service users reported using visualizations to communicate.

Increased Self-awareness
• Identifying patterns • Seeing progress Any description of how visualizations related to or impacted service users' self-awareness, usually regarding symptoms and triggers. Sub codes describe the use of visualizations to identify patterns (e.g., identify responses to a trigger, relating specific activities to symptoms) or seeing progress (e.g., seeing change over time or in response to an intervention)

Validate current feelings/experiences
Any description of how visualizations impacted services users' perception of the validity, acceptability, normality, or realness of their own symptoms Improve recall of past experiences Any description of how visualizations impact (actually or hypothetically) service users' ability to remember or recount historical symptoms or experiences

Provide structure & organization
Any description of how visualizations impact (actually or hypothetically) service users' ability to organize or structure their memories, symptom data, or approach to self-management

Affecting self-image
Any description of how visualizations impact (actually or hypothetically) service users' perception of themselves, their illness, or their abilities, either positively or negatively Engagement with RMT Any description of how visualizations impact (actually or hypothetically) engagement with remote monitoring technologies, either within a single session of using the RMT or over time.

Enable proactive self-management
Any description of how visualizations impact (actually or hypothetically) participants' ability or motivation to selfmanage their conditions

Moderators of visualization preferences/needs Health status
Any description of how health status impacts (actually or hypothetically) visualization design preferences. This could include impact of a user's condition or disease severity (ie, preferences in moderate vs. Severe disease), differing preferences before/during/after relapses or episodic events, or preferences in times of wellness vs. Those while feeling unwell.

Personas
• Data person • Non data person • Any discussion of individuals being the 'type of person' who identifies with or prefers numbers, data, analytics, or qualitative reporting and visualization methods

Experience with health monitoring
Any discussion of how past experiences with health tracking or mHealth applications impact (actually or hypothetically) current design preferences

Context/Intended use
Any discussion of how context impacts (actually or hypothetically) current design preferences

Customization Desire for customization
Indication of a desire (or lack thereof) for customizing features of data visualizations

Individual needs and interests
Any discussion of customization, flexibility, personalization, or conflicting opinions due to individual experiences, priorities, and preferences

Responsiveness to current status
Any discussion of differing needs or preferences in specific situations (ie, when I'm depressed, do/don't show me…)

Data reporting Translating feelings into objective information
Any discussion of using visualizations to translate abstract experiences, feelings, or symptoms into objective data

Identifying with data reporting mechanisms
Any discussion of how a service user perceives their relationship with a data reporting mechanism

Accuracy of quantitative vs. Qualitative data
Any discussion on the perceived accuracy or validity of quantitative or qualitative data, within the context of visualization-based mechanisms Author response to service user feedback Author response to feedback Any description of design features or changes authors incorporated in response to service users' input or feedback

Increased selfawareness
"I found the most valuable tool to analyze my activities. It provides an understanding of which activities helps me, and which gives problems that I need to be aware of -or completely avoid." [31] (service user quote, depression)

Enable proactive selfmanagement
"A majority of respondents described this awareness as an opportunity to become proactive about their condition, helping them make adjustments to preempt mood episode triggers and maintain stability or at least avoid severe episodes. Survey respondents also stated that the feedback provided by tracking keeps them accountable to themselves and that they find visual forms of feedback particularly helpful for identifying personal behavioral and emotional patterns and motivating positive lifestyle choices." [46] (author analysis of service user responses, bipolar disorder)